Recent work has started to develop a formal account of collective intelligence to explain the relationship between individual interaction and collective performance using (approximate or variational) Bayesian inference [1]}, [2]}, . The free energy principle is a mathematical framework for multiscale behavioral processes that suggests a system of self-similar agents self-organizes by minimizing variational free energy in its exchanges with the environment fig:conceptuala;friston2006free,badcock2019hierarchically. Recent extensions have applied the framework to explain human communication [3]}. A key advantage of this approach is that free energy minimization can be translated into an generative, agent-based process theory [4]}, [5]}.
This generative theory provides a computational approach to cognition [6]}, [7]} that allows us to simultaneously (a) build agents for Human-AI teams that are highly explainable [8]} but also (b) test theories about human cognitive processes and measure (sense) human theory of mind ability in real time. This promises to advance our understanding of a key process of human collective intelligence. The current state of the art to measure theory of mind—the Reading the Mind in the Eyes test [9]}—is an indirect, survey based instrument, which typically explains about 3% of variation [10]}. We contribute to this body of research which has so far mostly used toy models—often using just a single agent—of simulated emergence of collective behavior with an application to real data from multi-human communication [3]}, [5]}, [13]}, [14]}.
